{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHAPTER 14 Data Analysis Examples（数据分析实例）\n",
    "\n",
    "# 14.1 USA.gov Data from Bitly（USA.gov数据集）\n",
    "\n",
    "2011年，短链接服务（URL shortening service）商[Bitly](https://bitly.com/)和美国政府网站[USA.gov](https://www.usa.gov/)合作，提供了一份从用户中收集来的匿名数据，这些用户使用了结尾为.gov或.mil的短链接。在2011年，这些数据的动态信息每小时都会保存一次，并可供下载。不过在2017年，这项服务被停掉了。\n",
    "\n",
    "数据是每小时更新一次，文件中的每一行都用JOSN（JavaScript Object Notation）格式保存。我们先读取几行看一下数据是什么样的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = '../datasets/bitly_usagov/example.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11 (KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\": \"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(path).readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "python有很多内置的模块能把JSON字符串转换成Python字典对象。这里我们用JSON模块："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "path = '../datasets/bitly_usagov/example.txt'\n",
    "records = [json.loads(line) for line in open(path)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面这种方法叫做列表推导式, list comprehension, 在一组字符串上执行一条相同操作（比如这里的json.loads）。结果对象records现在是一个由dict组成的list："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n",
       " 'al': 'en-US,en;q=0.8',\n",
       " 'c': 'US',\n",
       " 'cy': 'Danvers',\n",
       " 'g': 'A6qOVH',\n",
       " 'gr': 'MA',\n",
       " 'h': 'wfLQtf',\n",
       " 'hc': 1331822918,\n",
       " 'hh': '1.usa.gov',\n",
       " 'l': 'orofrog',\n",
       " 'll': [42.576698, -70.954903],\n",
       " 'nk': 1,\n",
       " 'r': 'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
       " 't': 1331923247,\n",
       " 'tz': 'America/New_York',\n",
       " 'u': 'http://www.ncbi.nlm.nih.gov/pubmed/22415991'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'America/New_York'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]['tz']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Counting Time Zones in Pure Python（用纯python代码对时区进行计数）\n",
    "\n",
    "我们想知道数据集中出现在哪个时区（即tz字段）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'tz'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-db4fbd348da9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtime_zones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tz'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-10-db4fbd348da9>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtime_zones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tz'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'tz'"
     ]
    }
   ],
   "source": [
    "time_zones = [rec['tz'] for rec in records]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看来并不是所有的记录都有时区字段。那么只需要在推导式的末尾加一个if 'tz' in rec判断即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_zones = [rec['tz'] for rec in records if 'tz' in rec]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['America/New_York',\n",
       " 'America/Denver',\n",
       " 'America/New_York',\n",
       " 'America/Sao_Paulo',\n",
       " 'America/New_York',\n",
       " 'America/New_York',\n",
       " 'Europe/Warsaw',\n",
       " '',\n",
       " '',\n",
       " '']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_zones[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这10条时区信息中，可以看到有些是空字符串，现在先留着。\n",
    "\n",
    "为了对时区进行计数，我们用两种方法：一个用纯python代码，比较麻烦。另一个用pandas，比较简单。 这里我们先介绍使用纯python代码的方法：\n",
    "\n",
    "遍历时区的过程中将计数值保存在字典中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_counts(sequence):\n",
    "    counts = {}\n",
    "    for x in sequence:\n",
    "        if x in counts:\n",
    "            counts[x] += 1\n",
    "        else:\n",
    "            counts[x] = 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用python标准库的话，能把代码写得更简洁一些："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def get_counts2(sequence):\n",
    "    counts = defaultdict(int) # 所有的值均会被初始化为0\n",
    "    for x in sequence:\n",
    "        counts[x] += 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（译者：下面关于defaultdict的用法是我从Stack Overflow上找到的，英文比较多，简单的说就是通常如果一个字典里不存在一个key，调用的时候会报错，但是如果我们设置了了default，就不会被报错，而是会新建一个key，对应的value就是我们设置的int，这里int代表0）\n",
    "\n",
    "> **defaultdict** means that if a key is not found in the dictionary, then instead of a KeyError being thrown, a new entry is created. The type of this new entry is given by the argument of defaultdict.\n",
    "\n",
    "    somedict = {}\n",
    "    print(somedict[3]) # KeyError\n",
    "\n",
    "    someddict = defaultdict(int)\n",
    "    print(someddict[3]) # print int(), thus 0\n",
    "\n",
    "\n",
    ">Usually, a Python dictionary throws a KeyError if you try to get an item with a key that is not currently in the dictionary. The defaultdict in contrast will simply create any items that you try to access (provided of course they do not exist yet). To create such a \"default\" item, it calls the function object that you pass in the constructor (more precisely, it's an arbitrary \"callable\" object, which includes function and type objects). For the first example, default items are created using `int()`, which will return the integer object 0. For the second example, default items are created using `list()`, which returns a new empty list object.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "someddict = defaultdict(int)\n",
    "print(someddict[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "someddict[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面用函数的方式写出来是为了有更高的可用性。要对它进行时区处理，只需要将time_zones传入即可："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counts = get_counts(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts['America/New_York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如何想要得到前10位的时区及其计数值，我们需要一些有关字典的处理技巧："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def top_counts(count_dict, n=10):\n",
    "    value_key_pairs = [(count, tz) for tz, count in count_dict.items()]\n",
    "    value_key_pairs.sort()\n",
    "    return value_key_pairs[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(33, 'America/Sao_Paulo'),\n",
       " (35, 'Europe/Madrid'),\n",
       " (36, 'Pacific/Honolulu'),\n",
       " (37, 'Asia/Tokyo'),\n",
       " (74, 'Europe/London'),\n",
       " (191, 'America/Denver'),\n",
       " (382, 'America/Los_Angeles'),\n",
       " (400, 'America/Chicago'),\n",
       " (521, ''),\n",
       " (1251, 'America/New_York')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果用python标准库里的collections.Counter类，能让这个任务变得更简单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts = Counter(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('America/New_York', 1251),\n",
       " ('', 521),\n",
       " ('America/Chicago', 400),\n",
       " ('America/Los_Angeles', 382),\n",
       " ('America/Denver', 191),\n",
       " ('Europe/London', 74),\n",
       " ('Asia/Tokyo', 37),\n",
       " ('Pacific/Honolulu', 36),\n",
       " ('Europe/Madrid', 35),\n",
       " ('America/Sao_Paulo', 33)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Counting Time Zones with pandas（用pandas对时区进行计数）\n",
    "\n",
    "从一组原始记录中创建DataFrame是很简单的，直接把records传递给pandas.DataFrame即可：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3560 entries, 0 to 3559\n",
      "Data columns (total 18 columns):\n",
      "_heartbeat_    120 non-null float64\n",
      "a              3440 non-null object\n",
      "al             3094 non-null object\n",
      "c              2919 non-null object\n",
      "cy             2919 non-null object\n",
      "g              3440 non-null object\n",
      "gr             2919 non-null object\n",
      "h              3440 non-null object\n",
      "hc             3440 non-null float64\n",
      "hh             3440 non-null object\n",
      "kw             93 non-null object\n",
      "l              3440 non-null object\n",
      "ll             2919 non-null object\n",
      "nk             3440 non-null float64\n",
      "r              3440 non-null object\n",
      "t              3440 non-null float64\n",
      "tz             3440 non-null object\n",
      "u              3440 non-null object\n",
      "dtypes: float64(4), object(14)\n",
      "memory usage: 500.7+ KB\n"
     ]
    }
   ],
   "source": [
    "frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     America/New_York\n",
       "1       America/Denver\n",
       "2     America/New_York\n",
       "3    America/Sao_Paulo\n",
       "4     America/New_York\n",
       "5     America/New_York\n",
       "6        Europe/Warsaw\n",
       "7                     \n",
       "8                     \n",
       "9                     \n",
       "Name: tz, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['tz'][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里frame的输出形式是summary view, 主要用于较大的dataframe对象。frame['tz']所返回的Series对象有一个value_counts方法，该方法可以让我们得到想要的信息:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tz_counts = frame['tz'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "                        521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "America/Sao_Paulo        33\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们能利用matplotlib为这段数据生成一张图片。这里我们先给记录中未知或缺失的时区填上一个替代值。fillna函数可以替代缺失值（NA），而未知值（空字符串）则可以通过布尔型数组索引，加以替换："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_tz = frame['tz'].fillna('Missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_tz[clean_tz == ''] = 'Unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tz_counts = clean_tz.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "Unknown                 521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Missing                 120\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用counts对象的plot方法即可得到一张水平条形图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fba90b8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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krQX8EtjO9hJ9ESPHTe1xv2a5o03OkTSm1NKYUkuf22zpbP2KcqFNw5G0M3AO8G9LGogA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\nERFFQjEiIqJIKEZERBQJxYiIiOL/A8E9ZqsYIeE9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc68908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "tz_counts[:10].plot(kind='barh', rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然，我们也可以使用之前介绍的seaborn来画一个水平条形图（horizontal bar plot）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fc93fd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FnElaYr2giXH/F/h3SXMj4rO57SXg7xHxSH79BtANGA/cEBEPkxLv8uYmKenZ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\nljkpmpmZZU6KZmZmmZOimZlZ9v/+jFe67PC+YAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fcd5358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = tz_counts[:10]\n",
    "sns.barplot(y=subset.index, x=subset.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们还可以对这种数据进行更多的处理。比如a字段含有执行URL操作的浏览器、设备、应用程序的相关信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GoogleMaps/RochesterNY'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P925/V10e Build/FRG83G) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][51]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...\n",
       "1                               GoogleMaps/RochesterNY\n",
       "2    Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...\n",
       "3    Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...\n",
       "4    Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...\n",
       "Name: a, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将这些USER_AGENT字符串中的所有信息都解析出来是一件挺郁闷的工作。不过只要掌握了Python内置的字符串函数和正则表达式，就方便了。比如，我们可以把字符串的第一节（与浏览器大致对应）分离出来得到另一份用户行为摘要："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = Series([x.split()[0] for x in frame.a.dropna()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Mozilla/5.0\n",
       "1    GoogleMaps/RochesterNY\n",
       "2               Mozilla/4.0\n",
       "3               Mozilla/5.0\n",
       "4               Mozilla/5.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mozilla/5.0                 2594\n",
       "Mozilla/4.0                  601\n",
       "GoogleMaps/RochesterNY       121\n",
       "Opera/9.80                    34\n",
       "TEST_INTERNET_AGENT           24\n",
       "GoogleProducer                21\n",
       "Mozilla/6.0                    5\n",
       "BlackBerry8520/5.0.0.681       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.value_counts()[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，假设我们想按Windows和非Windows用户对时区统计信息进行分解。为了简单期间，我们假定只要agent字符串中含有“windows”就认为该用户是windows用户。由于有的agent缺失，所以先将他们从数据中移除："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_heartbeat_</th>\n",
       "      <th>a</th>\n",
       "      <th>al</th>\n",
       "      <th>c</th>\n",
       "      <th>cy</th>\n",
       "      <th>g</th>\n",
       "      <th>gr</th>\n",
       "      <th>h</th>\n",
       "      <th>hc</th>\n",
       "      <th>hh</th>\n",
       "      <th>kw</th>\n",
       "      <th>l</th>\n",
       "      <th>ll</th>\n",
       "      <th>nk</th>\n",
       "      <th>r</th>\n",
       "      <th>t</th>\n",
       "      <th>tz</th>\n",
       "      <th>u</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Danvers</td>\n",
       "      <td>A6qOVH</td>\n",
       "      <td>MA</td>\n",
       "      <td>wfLQtf</td>\n",
       "      <td>1.331823e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orofrog</td>\n",
       "      <td>[42.576698, -70.954903]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Provo</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>1.308262e+09</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>US</td>\n",
       "      <td>Washington</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>DC</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>1.331920e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[38.9007, -77.043098]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://t.co/03elZC4Q</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://boxer.senate.gov/en/press/releases/0316...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>pt-br</td>\n",
       "      <td>BR</td>\n",
       "      <td>Braz</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>27</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>[-23.549999, -46.616699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>1.273672e+09</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   _heartbeat_                                                  a  \\\n",
       "0          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "1          NaN                             GoogleMaps/RochesterNY   \n",
       "2          NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "3          NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "4          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "\n",
       "               al   c          cy       g  gr       h            hc  \\\n",
       "0  en-US,en;q=0.8  US     Danvers  A6qOVH  MA  wfLQtf  1.331823e+09   \n",
       "1             NaN  US       Provo  mwszkS  UT  mwszkS  1.308262e+09   \n",
       "2           en-US  US  Washington  xxr3Qb  DC  xxr3Qb  1.331920e+09   \n",
       "3           pt-br  BR        Braz  zCaLwp  27  zUtuOu  1.331923e+09   \n",
       "4  en-US,en;q=0.8  US  Shrewsbury  9b6kNl  MA  9b6kNl  1.273672e+09   \n",
       "\n",
       "          hh   kw         l                        ll   nk  \\\n",
       "0  1.usa.gov  NaN   orofrog   [42.576698, -70.954903]  1.0   \n",
       "1       j.mp  NaN     bitly  [40.218102, -111.613297]  0.0   \n",
       "2  1.usa.gov  NaN     bitly     [38.9007, -77.043098]  1.0   \n",
       "3  1.usa.gov  NaN  alelex88  [-23.549999, -46.616699]  0.0   \n",
       "4     bit.ly  NaN     bitly   [42.286499, -71.714699]  0.0   \n",
       "\n",
       "                                                   r             t  \\\n",
       "0  http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...  1.331923e+09   \n",
       "1                           http://www.AwareMap.com/  1.331923e+09   \n",
       "2                               http://t.co/03elZC4Q  1.331923e+09   \n",
       "3                                             direct  1.331923e+09   \n",
       "4                http://www.shrewsbury-ma.gov/selco/  1.331923e+09   \n",
       "\n",
       "                  tz                                                  u  \n",
       "0   America/New_York        http://www.ncbi.nlm.nih.gov/pubmed/22415991  \n",
       "1     America/Denver        http://www.monroecounty.gov/etc/911/rss.php  \n",
       "2   America/New_York  http://boxer.senate.gov/en/press/releases/0316...  \n",
       "3  America/Sao_Paulo            http://apod.nasa.gov/apod/ap120312.html  \n",
       "4   America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cframe = frame[frame.a.notnull()]\n",
    "cframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其次根据a值计算出各行是否是windows："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xu/anaconda/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "cframe['os'] = np.where(cframe['a'].str.contains('Windows'), \n",
    "                            'Windows', 'Not Windows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Windows\n",
       "1    Not Windows\n",
       "2        Windows\n",
       "3    Not Windows\n",
       "4        Windows\n",
       "Name: os, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cframe['os'][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来就可以根据时区和新得到的操作系统列表对数据进行分组了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_tz_os = cframe.groupby(['tz', 'os'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz                              os         \n",
       "                                Not Windows    245\n",
       "                                Windows        276\n",
       "Africa/Cairo                    Windows          3\n",
       "Africa/Casablanca               Windows          1\n",
       "Africa/Ceuta                    Windows          2\n",
       "Africa/Johannesburg             Windows          1\n",
       "Africa/Lusaka                   Windows          1\n",
       "America/Anchorage               Not Windows      4\n",
       "                                Windows          1\n",
       "America/Argentina/Buenos_Aires  Not Windows      1\n",
       "America/Argentina/Cordoba       Windows          1\n",
       "America/Argentina/Mendoza       Windows          1\n",
       "America/Bogota                  Not Windows      1\n",
       "                                Windows          2\n",
       "America/Caracas                 Windows          1\n",
       "America/Chicago                 Not Windows    115\n",
       "                                Windows        285\n",
       "America/Chihuahua               Not Windows      1\n",
       "                                Windows          1\n",
       "America/Costa_Rica              Windows          1\n",
       "America/Denver                  Not Windows    132\n",
       "                                Windows         59\n",
       "America/Edmonton                Not Windows      2\n",
       "                                Windows          4\n",
       "America/Guayaquil               Not Windows      2\n",
       "America/Halifax                 Not Windows      1\n",
       "                                Windows          3\n",
       "America/Indianapolis            Not Windows      8\n",
       "                                Windows         12\n",
       "America/La_Paz                  Windows          1\n",
       "                                              ... \n",
       "Europe/Madrid                   Not Windows     16\n",
       "                                Windows         19\n",
       "Europe/Malta                    Windows          2\n",
       "Europe/Moscow                   Not Windows      1\n",
       "                                Windows          9\n",
       "Europe/Oslo                     Not Windows      2\n",
       "                                Windows          8\n",
       "Europe/Paris                    Not Windows      4\n",
       "                                Windows         10\n",
       "Europe/Prague                   Not Windows      3\n",
       "                                Windows          7\n",
       "Europe/Riga                     Not Windows      1\n",
       "                                Windows          1\n",
       "Europe/Rome                     Not Windows      8\n",
       "                                Windows         19\n",
       "Europe/Skopje                   Windows          1\n",
       "Europe/Sofia                    Windows          1\n",
       "Europe/Stockholm                Not Windows      2\n",
       "                                Windows         12\n",
       "Europe/Uzhgorod                 Windows          1\n",
       "Europe/Vienna                   Not Windows      3\n",
       "                                Windows          3\n",
       "Europe/Vilnius                  Windows          2\n",
       "Europe/Volgograd                Windows          1\n",
       "Europe/Warsaw                   Not Windows      1\n",
       "                                Windows         15\n",
       "Europe/Zurich                   Not Windows      4\n",
       "Pacific/Auckland                Not Windows      3\n",
       "                                Windows          8\n",
       "Pacific/Honolulu                Windows         36\n",
       "Length: 149, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_tz_os.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面通过size对分组结果进行计数，类似于value_counts函数，并利用unstack对计数结果进行重塑为一个表格："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "agg_counts = by_tz_os.size().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Cairo</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Casablanca</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Ceuta</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Johannesburg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Lusaka</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Anchorage</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Buenos_Aires</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Cordoba</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Mendoza</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                              Not Windows  Windows\n",
       "tz                                                  \n",
       "                                      245.0    276.0\n",
       "Africa/Cairo                            0.0      3.0\n",
       "Africa/Casablanca                       0.0      1.0\n",
       "Africa/Ceuta                            0.0      2.0\n",
       "Africa/Johannesburg                     0.0      1.0\n",
       "Africa/Lusaka                           0.0      1.0\n",
       "America/Anchorage                       4.0      1.0\n",
       "America/Argentina/Buenos_Aires          1.0      0.0\n",
       "America/Argentina/Cordoba               0.0      1.0\n",
       "America/Argentina/Mendoza               0.0      1.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，我们来选取最常出现的时区。为了达到这个目的，根据agg_counts中的行数构造了一个简洁索引数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "                                  24\n",
       "Africa/Cairo                      20\n",
       "Africa/Casablanca                 21\n",
       "Africa/Ceuta                      92\n",
       "Africa/Johannesburg               87\n",
       "Africa/Lusaka                     53\n",
       "America/Anchorage                 54\n",
       "America/Argentina/Buenos_Aires    57\n",
       "America/Argentina/Cordoba         26\n",
       "America/Argentina/Mendoza         55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexer = agg_counts.sum(1).argsort()\n",
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "                                  24\n",
       "Africa/Cairo                      20\n",
       "Africa/Casablanca                 21\n",
       "Africa/Ceuta                      92\n",
       "Africa/Johannesburg               87\n",
       "Africa/Lusaka                     53\n",
       "America/Anchorage                 54\n",
       "America/Argentina/Buenos_Aires    57\n",
       "America/Argentina/Cordoba         26\n",
       "America/Argentina/Mendoza         55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexer = agg_counts.sum(1).argsort()\n",
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后通过take按照这个顺序截取了最后10行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>America/Sao_Paulo</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset = agg_counts.take(indexer)[-10:]\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas有一个很方便的方法叫nlargest，可以实现相同效果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "America/New_York       1251.0\n",
       "                        521.0\n",
       "America/Chicago         400.0\n",
       "America/Los_Angeles     382.0\n",
       "America/Denver          191.0\n",
       "Europe/London            74.0\n",
       "Asia/Tokyo               37.0\n",
       "Pacific/Honolulu         36.0\n",
       "Europe/Madrid            35.0\n",
       "America/Sao_Paulo        33.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts.sum(1).nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的输出结果可以画成条形图；通过给seaborn的barplot函数传递一个参数，来画出堆积条形图（stacked bar plot）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz                 os         \n",
       "America/Sao_Paulo  Not Windows    13.0\n",
       "                   Windows        20.0\n",
       "Europe/Madrid      Not Windows    16.0\n",
       "                   Windows        19.0\n",
       "Pacific/Honolulu   Not Windows     0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rearrange the data for plotting\n",
    "count_subset = count_subset.stack()\n",
    "count_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tz</th>\n",
       "      <th>os</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Windows</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Windows</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Windows</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  tz           os  total\n",
       "0  America/Sao_Paulo  Not Windows   13.0\n",
       "1  America/Sao_Paulo      Windows   20.0\n",
       "2      Europe/Madrid  Not Windows   16.0\n",
       "3      Europe/Madrid      Windows   19.0\n",
       "4   Pacific/Honolulu  Not Windows    0.0\n",
       "5   Pacific/Honolulu      Windows   36.0\n",
       "6         Asia/Tokyo  Not Windows    2.0\n",
       "7         Asia/Tokyo      Windows   35.0\n",
       "8      Europe/London  Not Windows   43.0\n",
       "9      Europe/London      Windows   31.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset.name = 'total'\n",
    "count_subset = count_subset.reset_index()\n",
    "count_subset[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fc5fcc0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vZNKk51m9ehXPPDORsWOzueuu+9m6NXQHv/jic+y9996MGTOO4cNH8fDDo8nMrM/y5d+w\nefNmZs6cTkZGJVavXsU777xFmzbtmDbtLdq3P5MHH3yI8877X77/vmyNySozraCyew5P2wcERKRo\nVKpUicMPP4KZM6ezzz77Uq1aNVq1OoXp06fx2WefcsEFXRkz5sEfj2/SJPyuvkGDBmzevJmvv/43\nhxxyKNWqVQP4ccaXL774gpNOagFAzZp70rjxIXz99b9p0eJk5s2by3fffUuHDp2YO3c2CxbM59JL\nr+TII4/m8cfHMXDg5WRm1ufII48u4dYomDJTERHJV/PmLXniifG0ahXmMj3mmONwX8T27dupXbvO\nz47NO9PLgQcexOef/4tNmzaybds2Fi8O75tp3LgxH3zwPgAbNvyXJUuW0LBhQ04/vS1PPpnNYYc1\noUWLk3n22f/jwAMPpEqVKrz++hQ6dz6HkSPHcsghhzJp0vOUJcpMRUTKidL46Vrz5i25++7buemm\n8NP5qlWrUqtWLQ4//IhCz61bty49elxM376XsPfeddljjz0A6NLlfO6++3Yuv7wXmzZt4pJL+lC3\n7j7UqbM3X321lO7dL+Lww5vw7bfL6d79YgCaNj2au+66nT322OPHmWLKEv3OtOLKUTfvjtL593Gp\nULskp3ZJLp3bRb8zFRERKSYKpiIiIilSMBUREUmRgqmIiEiKFExFRERSpGAqIiKSonL9O1Mzaws8\nTZgcO4cwn+gEdx+5k+c3AP7g7leY2a+AYcBIoK27n1/AeVcD84FxwC/cfWPc/gtgTJwoPGVmttzd\nG+SzrzFpNCWciEh5Vq6DaTTV3bsCmFl1wM3sCXdfW9iJ7r4cuCKu/hK42t1fAkYUcupphKArIiKS\nFsE0US1gG3Csmd1M6MbeC+jm7ovNbChwHuG+RwOvAROBO4HOwElmthJ4wd0bmFlL4IFYztdAd6Aa\nsMHdt5hZvhUxs7OA24GNwCrgEuA4YAiwGTiUkFneEbPMcbFeOcAAd1+QUNabQF93X2RmfYEGQHbC\n/i+IGbKZ3QUscvcf94uISPFKh2DaPgab7cAWoD9wFNDD3ZeZ2Q3ABWY2BTgbaAlUBv4EvA7g7pPM\n7HxCcJuRECTHAr9z94Vm1gtoChyee170upltj8s1gQ1mlgE8BJzm7l+b2UBgKDAZOBg4BqgOLAPu\nAO4Fhrv7i2Z2HPAocFKRtlIeWeMH7tLxpfEaMxGR8iIdgumP3by5zOxcYISZrQcOAN4FDJjt7tsI\n2es1MSMsSAN3Xwjg7o/GsvsDgxKO6ZB3zBSoB3zv7l/HY94mZL+TgQ/dfSuw1cx+iPubxmNw9/lm\n1qiAOiV9ldUu7BcRkSKWrk/zPgz0dPcsQvaXASwCTjCzSmZW1cz+TsgOC7LMzJoAmNkQM/s1sI+7\nryzkvJVAbTPbP663ARbH5WQvQ14ItI7XOQ5Ynmf/RiC3rBOSnL8R2D9mxMcVUjcRESli6ZCZJvMk\nMM3M/gt8CzSMGd+rhCy1EmHMdFMh5VwGjIvduN8AM4FZhV3c3XPMrA/wfDx3DZAF5DcB37XAw2Z2\nLVAV6JVn/whglJl9SRi7zWsYMAX4Il5LRERKkGaNqaCyxg/cpQ++ooyZpvNsF6lQuySndkkundtF\ns8aIiIgUEwVTERGRFCmYioiIpChdH0CSQmT3HJ62YxoiIiVNmamIiEiKFExFRERSpGAqIiKSIgVT\nERGRFCmYVlDdBk8o7SqIiKQNBVMREZEUKZiKiIikSMFUREQkRQqmIiIiKVIwFRERSZFeJ1gIMxsM\nXAUc4u4b8znmOmCqu88uoJyrgY+B6+OmU4Dpcfkad38vyTlnAlnu3iOFWxARkWKmYFq4HsBEoCuQ\nnewAd79rJ8o5DRjp7q8BmNlyd29bRHUUEZFSpGBaADNrCywBxgBPAtlmdgVwMbAdmOPuA8wsmxBw\npwOPAHsDDYG/uPtoM6sDbHD3LQVcqxNwK7AJWAFckrBvT+AFYBxwArDE3cea2b7Aq+7e3MweAE6O\npzzh7g8WdG/Vms5m0OR8E+kdVJTJwUVEdofGTAvWG3jE3R3YZGYtgZ5AP3c/GVhoZolfSA4HJrp7\nB6ADcHXc3hF4Pb+LmFklQsA+z91PJwTl3O7gWsBLwAPuPpEQrC+K+3oAT5jZeYTg3QpoDWSZ2ZGp\n3bqIiOwsBdN8mFldoDMw0MxeBeoA/QjB9Eozews4GMhIOO1b4DwzexIYClSN288GphRwuf2AVe7+\nTVx/GzgqLrcF9gCqA7j7YmCzmR0B/I6QMTcFprl7jrtvBmbFbSIiUgIUTPPXA3jU3Tu4eyegJSHb\nvBzo6+5tgOMJDxLlugaYER8YegbIiFnnPu6+soBrfQfsa2b7xfU2wOK4/BJwPnC3mTWI2x4hdAl/\n7u6rgYWEMVnMrCqhu/fT3b91ERHZFQqm+esNPJG74u4bgOcI2ec0M5tKCIKzEs55iZ+y1t8DWwnd\nronH7MDdtwGXAS+a2bvA6cAdCfu/Af4IPBo3PUfoOn4krr8ILDOz6cBM4K/u/sFu3LOIiOyGjJyc\nnNKug+wiM9sL+CfQwt136wPMGj9wl86rKA8gZWbWYsWKdaVdjTJH7ZKc2iW5dG6XzMxaGcm2KzMt\nZ8ysNTADuHN3A6mIiBQt/TSmnHH3aUCz0q6HiIj8RJmpiIhIipSZVlDZPYen7ZiGiEhJU2YqIiKS\nIgVTERGRFCmYioiIpEhjphVUt8ETdvvc4YO6FGFNRETKP2WmIiIiKVIwFRERSZGCqYiISIoUTEVE\nRFKkYCoiIpIiBVMREZEUpdVPY8ysLfA08EnC5hXufkEJ1uEl4D7CBOJdi6jMTkBXd88qivJERKRo\npVUwjaYWVRDbVWZ2EPBlaVxbRERKTzoG0x2Y2ZuETHGRmfUFGgDZwEvAKmAK8HdgJLAN2Aj0IXSD\nPwN8AxwIvOLuN5pZI+AhYA/gB+BSd/8KOAd4uYB6nAXcHstfBVwCHAcMATYDhwIT3f0OM2sKjAP+\nG/+siWV0B34PbAI+BS4FugOdgZrAYcDd7p5dUJtUazq70HbLVVEmBhcR2V3pOGba3szeTPgzqIBj\nGwAd3H0Y8DDQz93bAKOA++MxjYEsoHks+wTgXmCEu7eNy3fFY9sBU5NdyMwyCAH4/HiNt4ChcffB\nwK+BVsDguO0e4A/ufiYwPZaxL3Ar0N7dTwPWApfF4+u4+zlAF+C6AltIRESKVDpmpjt085rZ/ySs\nZiQsf+7um+NyQ3efH5ff5qcAucDdV8dyZgFGmJz7BjMbEsvbYmY1ge3uvtHMktWrHvC9u3+dcI07\ngcnAh+6+FdhqZj/E/UcAuenju0BTQub6sbuvSyijAzALyK37V0CN5E0jIiLFIR0z02Q2AvvH5RMS\ntm9PWF5mZsfE5TbA4rjc1MxqmllloCXh4aZFwJCYmV5G6Ao+E3ijgDqsBGqbWW49Eq+Rk+T4T4CT\n43Lz+PfnwJFmtudOliEiIiUgHTPT9nGMNNE9wCgz+xL4esdTgDBG+mDsjt0K9IrbNxOC5X7As+6+\nwMyuBUabWQ3CuOlAQlfwbQnldTCzuQnr3eI1njez7YQx0Czg6Hzqcw3wWOymXgFsdPeVZnYz8M9Y\nxmeELt1SeeBKRESCjJwcJTT5MbPGhAeCWpV2XYpa1viBO/3BV6QHkDIza7FixbrCD6xg1C7JqV2S\nS+d2ycyslZFse0Xp5hURESk26djNW2Tc/QvCE7YiIiL5UmYqIiKSokIzUzM70d3fy7PtAnd/pviq\nJcUtu+fwtB3TEBEpaTuTmc4xsxHxpyG5ri+uComIiJQ3OxNMPyL8HnOqmdWL25I+zSQiIlIR7Uww\n3eruvwceA2aY2YnAluKtloiISPmx00/zuvs4M1tEeIHBXsVXJSkJ3QZPKO0q7Jbhg7qUdhVERHaw\nM5npsbkL7j4dOB34sNhqJCIiUs7km5ma2QuEQLrVzP6V5xzN2SkiIhIV1M17MbAPMBwYkLB9K/Bt\ncVZKRESkPMk3mLr798D3wLklVx0REZHyR29AEhERSVHavJvXzAYDVwGHuPvGIiozC1jt7pN28bzz\ngb2Bi4CawAagKmE+0oHuvqoo6iciImVDOmWmPYCJFOHcnu6evauBNOoMvByXL3L3tu5+KvAK8FBR\n1U9ERMqGtMhMzawtsAQYAzwJZMcJwhcQJt9eD0wDOhIyxg5x2xigCeFLxVB3f9PMPgIWEyYFXwQs\nB8YCI4EWQDXgZmBy3N4I2B+Y5O5D4+Ti9d39WzP7WT3dfYKZ3REnFW8CjCC8TWoVcAlwPDAkXvtQ\nwpeDYcBC4Fh3/2+cmHwb8CwhMO8B/ABcClQGXorlTXH3YSk2rYiI7IS0CKZAb+ARd3cz22RmLeP2\n2e4+0MxeBTa4+1lm9hjQhhAAV7p7LzPbF3gbOIrwQoo/uvv7ZnZLLOc8oJ67tzCzusDVhEA90917\nx+D4b2Ao0ByYW0Bd1xAC+sPAJe7+iZn1AgYDfwcOBo4BqgPL3P0OM3sO+DXwONANOAsYBYxw91fM\n7AzgLuBGoAFwortvLqjBqjWdXVib7pKKNIG4iEhe5T6YxuDWGahvZv2BOkC/uHte/Hst8ElcXgPU\nAJoBrRMCb5WEdw973ssAMwDcfQ1wk5nVBpqbWTvCU8/V47HnAH/Lp64ZhGD3HdAUGBWz16rAp/Gw\nD919K+H3vT/EbY8Ao+MbqNzdV5lZM+AGMxtCyG5zX/H4eWGBVEREilY6jJn2AB519w7u3gloSejG\nzQRyCjhvEfCUu7cFzia8JnF13Lc9z7ELCRknZlbHzF4DsoC17t4duA+oGYPl8e4+j+R6AW+4+3ZC\nwL4oXn8woduYZHV2908JAXMQIaPNrf+QeP5lsf7J6i4iIsWs3GemhC7eC3NX3H1D7BbtXch5Y4GH\nzewtoDYwyt235x3njCYBZ5rZO4Q2u5XwFqi/mtnJwCZCZtkQWJbn3MfN7L9x+Wvgyrh8edxXhRBA\ne8Xz8/MocBvwz7h+LSFbrUEYNx1YyP2KiEgxycjJKSh5k3SVNX5gkX7w6TJmmplZS5OmJ6F2SU7t\nklw6t0tmZq2kU5CmQzeviIhIqVIwFRERSZGCqYiISIoUTEVERFKUDk/zym7I7jk8bR8QEBEpacpM\nRUREUqRgKiIikiIFUxERkRRpzLSC6jZ4QmlXYZcNH9SltKsgIpKUMlMREZEUKZiKiIikSMFUREQk\nRQqmIiIiKVIwFRERSVGxP81rZoOBq4BD3H1jEZWZBax290m7eN75wN7ARUBfd19UFPXJc42GwGfA\nxe7+TGHH70K52cBEd3+1qMoUEZGiURKZaQ9gItC1qAp09+xdDaRRZ+DloqpHPnoCI/hpEnAREUlz\nxZqZmllbYAkwBngSyDazN4EFwNHAemAa0JGQMXaI28YATQjBfqi7v2lmHwGLgc3AImA5MBYYCbQA\nqgE3A5Pj9kbA/sAkdx9qZhlAfXf/1syS1bUqMB44FKgM3O/u/2dmVwAXA9uBOe4+oID7zQAuBFoD\nL5rZ0e7+UcykOwM1gcOAu90928xaAH8B1gHfARvdPcvM+gPdgBxCNjoiTz2Ttc8dQDvCZ/qcu9+d\n7wcjIiJFqri7eXsDj7i7m9kmM2sZt89294Fm9iqwwd3PMrPHgDaEALjS3XuZ2b7A28BRwF7AH939\nfTO7JZZzHlDP3VuYWV3gakKgnunuvc2sBvBvYCjQHJhbQF0vA1a4ew8zqwXMM7M3CJnmFe4+x8wu\nN7Mq7r41nzLOAD509xVmNo6QnV4e99Vx945m1gR4CcgmBMUL3f3jGAwPMLMjgd8Cp8Xz/m5mr+Vp\n02Tt0x1oC3wDZBVwnwBUazq7sEPKnEGTy0+d7znn9tKugoiUoGILpjG4dQbqx0yrDtAv7p4X/14L\nfBKX1wA1gGZA64TAW8XM6sVlz3sZYAaAu68BbjKz2kBzM2sHfA9Uj8eeA/ytgCo3Bf4Ry1pnZp8Q\nssiewLVmdki8VkYBZfQBDolfEqoBx5rZdXHf/Pj3V/E+ARq6+8dxeRqhK/xo4GDgjbi9LiELzZVf\n+3QH7gIaAK8UUEcRESlixTlm2gN41N07uHsnoCWhGzeT0H2Zn0XAU+7eFjgbeAZYHfdtz3PsQkLG\niZnViRm3TUq4AAAQGUlEQVRcFrDW3bsD9wE1Y/fr8e4+j/wtJHTPEjPTZsDnhADZ193bAMcDpyQ7\nOQa0VkBLd+/k7u2B5wldxORzz1/FTJR4LoQvDB8D7WIbZAMfJJyTrH3WARcAvyN09WaZ2cEF3KuI\niBSh4gymvYEnclfcfQPwHD/PspIZC/zCzN4CpgNL3T1vEM01CVhjZu8ArwEPEDK6Tmb2NjAa+BRo\nCCzLc+6zZjY3/rkXeAjYN5b1JnCru38HfAhMM7OphHHNWfnU5SLCWOW2hG0PA1eQfzZ7BTDOzP5B\nGPfd4u4L4j28Y2ZzCe31dSHts4nwhWMm8E/gdeDLfK4pIiJFLCMnp6AkUYqTmV0JPB3HWG8HNrv7\nbSVx7azxA/XBF6N0GzPNzKylyeSTULskl87tkplZK2lypFljdpGZdSE86JTXcHd/YReL+xZ43czW\nA//hpy5hEREpRxRMd1H8fevu/MY1WVnPAs8WRVkiIlJ69DpBERGRFCkzraCyew5P2zGNVKTzWI+I\nFB9lpiIiIilSMBUREUmRgqmIiEiKFExFRERSpAeQKqhugyeUdhUKNHxQl9KugojITlNmKiIikiIF\nUxERkRQpmIqIiKRIwVRERCRFCqYiIiIpKrdP85rZYOAq4BB331hEZWYBq+PL7HflvPOBvQkTeV9H\nmLR7G2FC8AHu/qGZvUmYZHxRwnnHAV1Kato1EREpHuU2mAI9gIlAV0IQS5m77245nYEbgcFAPaCN\nu283s+bAi2Zm+VxvPjB/N68pIiJlRLkMpmbWFlgCjAGeBLJj5rcAOBpYD0wDOhIyxg5x2xigCaF7\ne6i7v2lmHwGLgc3AImA5MBYYCbQAqgE3A5Pj9kbA/sAkdx9qZhlAfXf/1swuBU509+0A7j7HzJq7\n+5YYT282s/2APYHfAQcRstWuZtYLuByoHMu+2cz6AefH41cCv4r7HwcaAl8Bp7t7QzM7PtZ5G7AR\n6OPuX+bXhtWazt71hi9BgyaX7foVpXSbSFykIiqvY6a9gUfc3YFNZtYybp/t7mcA1YEN7n4W8AnQ\nJp6z0t1PB84F/hLP2Qv4o7t3TSj/PKCeu7cA2gEnEYLoTHfvSAiyfeOxzYG5cbmmu69JrKi7r0pY\nfdnd2wOvAP+bu9HM6hO6h1sDJwDVzaw2sC9wpru3JHzxaQ5cCnzu7qcCtwD7xWIeBvq5extgFHB/\n4c0oIiJFodxlpmZWl9CtWt/M+gN1gH5x97z491pCEAVYA9QAmgGtEwJvFTOrF5c972WAGQAxON4U\ng1tzM2sHfE8I2ADnAH/LvZaZ1Xb37xPq+yvgjbj6Xvx7OdAg4XqHAh+5+w9x/bp47mbgKTNbDxwI\nVAWaAq/Gui0ysxXxnIax2xjgbeAuRESkRJTHzLQH8Ki7d3D3TkBLQjduJuGBn/wsAp5y97aEB4Se\nAVbHfdvzHLuQkAViZnXM7DUgC1jr7t2B+4CasYv3eHfPDeKPEbpyM+K5pxAyxNwHpPKr3xLgF2ZW\nPZ73rJm1Ac5z998C/QmfVQbwEXByPO4wwhgtwDIzOyYutyF0XYuISAkoj8G0N/BE7oq7bwCeI4yF\nFmQsIWC9BUwHluaObSYxiZBlvgO8BjxAyC47mdnbwGjgU8K45bKE8+4BNgEzzGwacDvhad3NBVXM\n3VcAdwNvmdkMQoY9B/ivmb0L/B34Jl7vUaBxrMct/BSo+wAPxusOJDzpLCIiJSAjJ6egZE7Kmpjt\n7uXur5tZE+BVdz9sV8vJGj9QH3wZUR4eQMrMrMWKFetKuxpljtoluXRul8zMWhnJtpe7MVPhX4Rx\n1JsJY6hXlnJ9REQqPAXTcsbdlxOeMBYRkTKiPI6ZioiIlCnKTCuo7J7D03ZMIxXpPNYjIsVHmamI\niEiKFExFRERSpGAqIiKSIo2ZVlDdBk8o7SqkneGDupR2FUSklCgzFRERSZGCqYiISIoUTEVERFKk\nYCoiIpIiBVMREZEUKZiKiIikSMFUREQkRQqmIiIiKdJLGyqoak1nl3YV0s6gyTvfpuVhQnAR2XnK\nTEVERFKkYCoiIpIiBVMREZEUKZiKiIikSMFUREQkRQqmIiIiKVIwFRERSZGCqYiISIr00oYKKrvn\ncFasWFfa1ShzMjNrqV1EZJcpMxUREUmRgqmIiEiKFExFRERSpDHTCqrb4AmlXQUpBsMHdSntKohU\nSMpMRUREUqRgKiIikiIFUxERkRQpmIqIiKRIwVRERCRFZe5pXjMbDFwFHOLuG4uozCxgtbtP2sXz\nzgf2Bi4C3nf3q+L2GsAid29cFPWLZd4L1HL3y+J6ZeBd4FZ3f2Unzs8GJrr7q0VVJxER2TllMTPt\nAUwEuhZVge6evauBNOoMvByXf2dmbYqqTkkMBU41szPj+iBgzs4EUhERKV1lKjM1s7bAEmAM8CSQ\nbWZvAguAo4H1wDSgIyFj7BC3jQGaEL4cDHX3N83sI2AxsBlYBCwHxgIjgRZANeBmYHLc3gjYH5jk\n7kPNLAOo7+7fmhnAQOAhMzsR2JpQ50bAQ8AewA/ApcDVwLvu/qyZvQq87u73m9nDwHh3n5733t19\no5ldBDxlZhcAFwCnxms0BsYRPq8cYIC7LzCzpfHePkmoT0tgBHCBu3+5Sx+AiIjsljIVTIHewCPu\n7ma2KQYGgNnuPjAGpg3ufpaZPQa0IQTAle7ey8z2Bd4GjgL2Av7o7u+b2S2xnPOAeu7ewszqEoLe\nAmCmu/eO3bf/JmSJzYG5CXVbADwO3A8MSNh+LzDC3V8xszOAuwjB+WIzexmoC5xhZn8GTiQE26Tc\nfZ6ZTQDeAM5M6Oa+Fxju7i+a2XHAo8BJhC8AJ7j7qtjNewpwBvBLd/+uoIau1nR2QbulnBo0WZ+r\nSEHuOef2Yim3zATTGNw6A/XNrD9QB+gXd8+Lf6/lpyxsDVADaAa0Tgi8VcysXlz2vJcBZgC4+xrg\nJjOrDTQ3s3bA90D1eOw5wN/ynH8XYRzz7IRtzYAbzGwIkAFsAd4BhgPtgOeA/wVaAzPcPaeQpngc\n6OzuCxK2NSV8ScDd58dsGMKXiFUJx3UAasU6iIhICSlLY6Y9gEfdvYO7dwJaEoJDJqFrMz+LgKfc\nvS0hyD0DrI77tuc5diEh48TM6pjZa0AWsNbduwP3ATVjF+/x7j4v8WR33wZcDPw5z/WHxOtfBjzj\n7tsJWe1g4HVCcB0GPL9TLbGjhYRgTMxMl+dzf7fEuo3azeuIiMhuKEvBtDfwRO6Ku28gZHVNCjlv\nLPALM3sLmA4sjcEsmUnAGjN7B3gNeIDQpdrJzN4GRgOfAg2BZckKcHfn58H0WuDmeP3HgQ/i9ucJ\nGeWCeK3DgbcKuZf8XAv0T6hjr/wOdPdHgH3MrNtuXktERHZRRk5OYb2Oko6yxg/UBy8iFU6qY6aZ\nmbUykm0vM2OmFYWZ/QFon2RXT3f/vKTrIyIiqVMwLWHufhtwW2nXQ0REik5ZGjMVEREplxRMRURE\nUqQHkCqunBUr1pV2HcqczMxaqF12pHZJTu2SXDq3S34PICkzFRERSZEyUxERkRQpMxUREUmRgqmI\niEiKFExFRERSpGAqIiKSIgVTERGRFCmYioiIpEjBVEREJEV60X0FYmaVCBOHHwtsAnq7+2elW6uS\nZWZVgXFAY6A6cDvwCZBNmIT+I+BKd99uZn0IE75vBW5398mlUeeSYmb1gfeAswj3nE0FbxMAM7se\n6AJUI/z7eYsK3Dbx39BjhH9D24A+6L8XZaYVzHlADXc/GbgOuK+U61MaegCr3L010Al4ELgfGBq3\nZQDnmlkDYABwKtAR+JOZVS+lOhe7+D/IscAPcVOFbxMAM2sLnEK45zZAI9Q2nYEq7n4KYQasO1Cb\nKJhWMKcBrwK4+0zgpNKtTql4BrgpLmcQvjGfSMg2AF4BzgRaAO+6+yZ3/w/wGXBMCde1JN0LjAGW\nxXW1SdAR+BB4AXgJmIzaZjFQJfZ01Qa2oDZRMK1gagP/SVjfZmYVqqvf3de7+zozqwU8CwwFMtw9\n972a64A67NhWudvTjpllASvc/bWEzRW6TRLUI3zpvADoC0wAKlXwtllP6OJdBDwMjED/vSiYVjDf\nA7US1iu5+9bSqkxpMbNGwD+BJ9z9r8D2hN21gLXs2Fa529PRJcBZZvYmcBzwOFA/YX9FbJNcq4DX\n3H2zuzuwkZ8HhIrYNlcR2uQIwvMXjxHGk3NVxDZRMK1g3iWMd2BmrQjdVxWKme0HvA4McfdxcfP7\ncWwM4GxgGjAbaG1mNcysDtCU8GBF2nH30929jbu3BeYDFwGvVOQ2SfAO0MnMMsysIbAn8EYFb5s1\n/JRxrgaqUsH/DYFmjalQEp7mPYYwXtjT3ReVbq1KlpkNB35L6KLKNZDQVVUNWAj0cfdt8UnESwlf\nOu909+dKur4lLWanfQnZ+sOoTTCzYUA7wj3fAHxOBW4bM9uL8ET8/oQ2GA7MpQK3CSiYioiIpEzd\nvCIiIilSMBUREUmRgqmIiEiKFExFRERSpGAqIiKSogr19hsRKR/M7ATgt+4+xMzGA7e4+9ICjr8U\nWOfuTxVwzC1x8TbgOeBCd19fhNWWCkyZqYiURX8G7o7L7Qi/iy7IKYRZgArl7rm/of3DbtdOJA9l\npiJSqPh2mxsJQe0wwnuN/0OYiSiD8Gat4wlZX1XCiw36uPsqM7sAuAbYI/7p7e5vxxdEzAZaA5lA\nf3d/xczaA9+4+2ozuw5oCEwxs9ZAE8JLAmoAKwnTezUmTJHW3sy+Ab4GRgJ7EV6LeJ+7j8hzS68B\nI8zsdnf/vijbSiomZaYisrNaAj2Bo4DLCS/HPwn4gPDWpLuAju5+PCFY3R3futUXOMfdj43HDEoo\ns1qcEvAqwtyyEALj2wDufhdhJpvOhBelTwT6xbLGAE+5+z+AScAf4sv6exPmzmxOyGrvyHsj7r4t\n1rtdUTSMiIKpiOysj9z9K3ffQMgK34jblwK/BA4C/mlm84F+QJPYpforoKOZ3QZkETLGXK/mlg3s\nE5ebAP9Ocv0jgDXuPgfA3Z8BDo/vfU10DVAjTup9R57rJVoaryWSMnXzisjO2pxnPXHGocrAO+7e\nBcDMagC14ntc5wBPELLNDwiBNtfG+HcOP42Lbs9Tdq5kX/4z4rUTPU14GftLhEy2az73s4Wfzxgk\nstuUmYpIUZgFnGxmR8T1m4B7CNnkduBOYCphRpG8wS+vJcDBCetbCV/8HdjXzJoDmNlvgKXuvjrh\nGICzCF2+LwJt4rHJrnkIYcJqkZQpMxWRorCcMC/q0zFw/RvoQZi/cj5hlp4NwFv8PFAm8xLhwaLR\ncX0yMAXoSJjx50Ez25Mw/ddv4zH/AO40s7XALcA7cdmBLwiB80exjicAF+/W3YrkoVljRKRMMbMM\nwjyi57r7ymK6xrnAae4+qNCDRXaCunlFpExx9xzg98CQ4ig/PmHcC/hjcZQvFZMyUxERkRQpMxUR\nEUmRgqmIiEiKFExFRERSpGAqIiKSIgVTERGRFP0/wRrB/PoI65wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a92c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='total', y='tz', hue='os', data=count_subset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于这张图中不太容易看清楚较小分组中windows用户的相对比例，因此我们可以将各行规范化为“总计为1”并重新绘图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def norm_total(group):\n",
    "    group['normed_total'] = group.total / group.total.sum()\n",
    "    return group\n",
    "\n",
    "results = count_subset.groupby('tz').apply(norm_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113ff5b70>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w93bAKYTHJK6M27bk2XchIePEzOqZ2ctANvCdu/cA7gZqxWB5mLvPI3+9gFfc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\nSEL/D11XqdlXu5aMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc9ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='normed_total', y='tz', hue='os', data=results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们还可以使用transform和groupby，来更有效率地计算规范化的和："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = count_subset.groupby('tz')\n",
    "results2 = count_subset.total / g.total.transform('sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "译者：下面的内容是不适用seaborn的画图方法，这种画法是2013年第一版中的内容："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>America/Sao_Paulo</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset = agg_counts.take(indexer)[-10:]\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里也可以生成一张条形图。我们使用stacked=True来生成一张堆积条形图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1143130b8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dGnWJiEjy6HGCIiIiMSmZioiIxKRkKiIiEpOSqYiISExKpiIiIjEpmYqIiMSkZCoiIhKT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\npiIiIjH9f8lh9JfulKm/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142dad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_subset.plot(kind='barh', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于这张图中不太容易看清楚较小分组中windows用户的相对比例，因此我们可以将各行规范化为“总计为1”并重新绘图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "normed_subset = count_subset.div(count_subset.sum(1), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11433a7b8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nkxdQt3l9MmrXYtW8z2napTVt+ndk6asfsWL2Ylr16VABr8C2qVZhKtvu6VtP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\nKUxFREQSUpiKiIgk9P/j9bS+uYRINAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114069438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normed_subset.plot(kind='barh', stacked=True)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [py35]",
   "language": "python",
   "name": "Python [py35]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
